Please use this identifier to cite or link to this item:
http://hdl.handle.net/10609/88605
Title: | Object recognition in images. A deep learning approach |
Author: | Rodríguez Olmos, Miguel Andrés |
Tutor: | Bosch Rue, Anna |
Abstract: | We employ methods from deep learning for image recognition. We use a dataset with +70k images and 73 classes in order to compare the performance of several well known deep network architectures. The approaches used include the full training of these networks and also the techniques of transfer learning and fine tuning with the weights pretrained on the ImageNet set. We show the superiority of the latter approach in our dataset. We also experiment with a reorganization of the labels in our dataset by grouping several classes shown by the confusion matrix to be indistinguishable for the models. In this case we obtain a classification accuracy score higher than 50%. |
Keywords: | computer vision deep learning image processing |
Document type: | info:eu-repo/semantics/masterThesis |
Issue Date: | Jan-2019 |
Publication license: | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
Appears in Collections: | Bachelor thesis, research projects, etc. |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
miguelyogurTFM0119memory.pdf | Memory of TFM | 6,69 MB | Adobe PDF | View/Open |
Share:
This item is licensed under a Creative Commons License